#coding=utf-8

import tensorflow as tf
import input_data

mnist = input_data.read_data_sets("/tmp/MNIST_data/", one_hot=True)


x = tf.placeholder('float', [None,784])

"""
初始化系数和向量，不初始化为0
"""
def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)

def bias_variable(shape):
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)


"""
strides 窗口在每一个维度上滑动的步长，一般为[1, stride,stride,1]
ksize：池化窗口的大小，取一个四维向量，一般是[1, height, width, 1]，
       因为我们不想在batch和channels上做池化，所以这两个维度设为了1
"""
def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')


"""
第4维为颜色通道，彩色为3，黑白为1
"""
x_image = tf.reshape(x, [-1,28,28,1])

"""
卷积层1,5x5 patch转32维特征
"""
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)


W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)


"""
由于池化采取的2x2的方式，图片缩减到7x7，采用1024个神经元的全连接层
"""
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

"""
drop out的概率
"""
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
y_ = tf.placeholder('float',shape=[None,10])

cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    for i in range(20000):
      batch = mnist.train.next_batch(50)
      if i%100 == 0:
        train_accuracy = accuracy.eval(feed_dict={
            x:batch[0], y_: batch[1], keep_prob: 1.0})
        print "step %d, training accuracy %g"%(i, train_accuracy)
      train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

    print "test accuracy %g"%accuracy.eval(feed_dict= \
    {x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})